By Robin Joy
Act 193 mandates that law enforcement agencies collect data on roadside stops for the purpose of evaluating racial disparities. The Act dictates agency data collection and any related conversation centers on agency behavior. The Act and the data collected do not focus on or reflect the stories told by Black, Indigenous and People of Color (BIPOC) as related to their contacts with law enforcement agencies. Because of Vermont’s rural nature, small populations, and policing strategies, we conclude that traffic stop and race data are not sufficient to inform policy makers and stakeholders. Rigorous qualitative research focused on the experiences of the BIPOC community which detects patterns and trends can distinguish structural issues within the criminal justice system. Agency data should be used as a supplement to that research. The purpose of the study was to test different methods of assessing racial disparities in traffic stops for their applicability for all Vermont law enforcement agencies. In short, we found that this was not possible. This report reviews the methodologies tested and the findings. On Measuring Disparities 1. We tested three peer reviewed methods for benchmarking the driving population: Commuting Hour populations, Resident Driver populations, and Crash Data benchmarking. All three failed in Vermont because of the state’s rural nature and small populations. The low volume of people of color makes it difficult for consistent analysis. It is not possible for one benchmarking standard to be applied to all law enforcement agencies in the state. 2. We can recommend the “Veil of Darkness” analysis as an effort to examine racial disparities. However, that analysis essentially measures one work shift in a police department. In some departments that may just be a single officer. 3. Post-stop outcome measures may be useful, however, without more information on the stop (such as the violation for which the person was ticketed/arrested and other circumstances surrounding the stop) it is of limited value. Further, because so few people are searched or arrested it is hard to draw a conclusion from the data. 4. Stop data will now include information as to how often the same person is stopped by a department. Specifically, the year, make, model, and color of the car and the town/state of residence and the state of the plate will be available. This will help illustrate the stories community members have spoken about in protests, legislative hearings, and news articles – stories of people who feel they are being continuously targeted. For example, using these additional data fields, researchers can identify a 30-year-old Asian female from Montpelier driving a 2008 White Honda CRV who has been stopped four times in one month for various reasons
Montpelier, VT: Crime Research Group, 2021, 27p.
